Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: Provided is a system and method for automated patient interaction. The method includes parsing a patient complaint comprising a plurality of words, determining a subset of patient queries from a plurality of patient queries based on the patient complaint and patient data, communicating the subset of patient queries to a first computing device; receiving, from the first computing device, responses to at least a portion of the subset of patient queries; generating output data based on the subset of patient queries and the responses; communicating the output data to a second computing device; receiving, from the second computing device, a user input corresponding to at least one patient query of the subset of patient queries; and training, based on the user input, at least one machine-learning algorithm configured to output at least one patient query based on at least one of the patient complaint and a subsequent patient complaint.

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: The disclosed system provides an efficient method of using a later word or words entered after a previous word along with one or more language models that show which words are likely to occur together to identify a better disambiguated choice for the previous word. To identify the better disambiguated choice for the previous word, the system can evaluate the conditional probability for the later word of various candidate previous words, and select the candidate previous word that has the highest conditional probability. If the conditional probability of the selected candidate previous word exceeds that of the previous word that was entered by at least a factor, then the system can include the selected candidate previous word in a selection list for user selection. The disclosed system also provides an efficient method for using one or more language models and a later word to correct errors in segmenting the word.

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: The disclosed system provides an efficient method of using a later word or words entered after a previous word along with one or more language models that show which words are likely to occur together to identify a better disambiguated choice for the previous word. To identify the better disambiguated choice for the previous word, the system can evaluate the conditional probability for the later word of various candidate previous words, and select the candidate previous word that has the highest conditional probability. If the conditional probability of the selected candidate previous word exceeds that of the previous word that was entered by at least a factor, then the system can include the selected candidate previous word in a selection list for user selection. The disclosed system also provides an efficient method for using one or more language models and a later word to correct errors in segmenting the word.

Abstract: A method and system for receiving text input via a computing device generates a graphical text element interface showing text elements arranged to provide for efficient selection by a user. Text elements show a single character, a group of characters, words, or phrases. And by selecting a text element, the user submits text in the computing device. The system may identify text elements to display based at least in part on a previous selection of a text element by a user.

Abstract: A method and system for receiving text input via a computing device generates a graphical text element interface showing text elements arranged to provide for efficient selection by a user. Text elements show a single character, a group of characters, words, or phrases. And by selecting a text element, the user submits text in the computing device. The system may identify text elements to display based at least in part on a previous selection of a text element by a user.

Abstract: The present technology describes context based text input, which uses linguistic models based on conditional probabilities to provide meaningful word completion and modification suggestions, such as auto-capitalization, based on previously entered words. The technology may use previously entered left context words to modify a list of candidate words matching a current user input. The left context may include one or more previously input words followed by a space, hyphen, or another word. The technology may then modify the list of candidate words based on one or more conditional probabilities, where the conditional probabilities show a probability of a candidate list modification given a particular left context. The modifying may comprise reordering the list or modifying properties of words on the list such as capitalization. The technology may then display the modified list of candidate words to the user.

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).